Time series prediction with a hybrid system formed by artificial neural network and cognitive development optimization algorithm

Document Type : Article


1 Computer Sciences Application and Research Center, Usak University, Usak, Turkey.

2 Department of Computer Engineering, Konya Food and Agriculture University, Konya, Turkey.


Time series prediction is a remarkable research interest, which is widely followed by scientists / researchers. Because many fields include analyzing processes over such time series, different kinds of approaches, methods, and techniques are often employed in order to achieve alternative prediction ways. It seems that Artificial Intelligence oriented solutions have strong potential on providing effective and accurate prediction approaches in even most complicated time series structures. In the sense of the explanations, this study aims to introduce an alternative, Artificial Intelligence based approach of Artificial Neural Networks, and Cognitive Development Optimization Algorithm, a recent intelligent optimization technique introduced by the authors. Here, it has been aimed to predict different kinds of time series, by using the introduced system / approach. In this way it has been possible to discuss about application potential of the hybrid system and report findings related to its success on prediction. The authors believe that the study has been a good chance to support the literature with an alternative solution approach and see potential of a newly developed, Artificial Intelligence oriented optimization algorithm on different applications.


Main Subjects

1. Douglas, A.I., Williams, G.M., Samuel, A.W., and Carol, A.W., Basic Statistics for Business and Economics, 3/e., McGraw-Hill (2009).
2. Esling, P. and Agon, C. "Time-series data mining", ACM Computing Surveys (CSUR), 45(1), p. 12 (2012).
3. NIST SEMATECH, Introduction to Time Series Analysis, In Engineering Statistics Handbook, http://www.itl.nist.gov/div898/handbook/pmc/ section 4/pmc4. htm Online. Retrieved on 10th July (2016).
4. Penn State Eberly Collage of Science, "Overview of time series characteristics", STAT-510, App. Time Series Analysis, https://onlinecourses. science. psu.edu/stat510/node/47, Online. Retrieved on 10th July (2016).
5. Gromov, G.A. and Shulga, A.N. "Chaotic time series prediction with employment of ant colony optimization", Expert Systems with App., 39, pp. 8474-8478 (2012).
6. Kose, U. and Arslan, A. "Realizing an optimization approach inspired from Piaget's theory on cognitive development", Broad Res. in artificial intelligence and Neuroscience, 6(1-2), pp. 14-21 (2015).
7. Piaget, J. "Part I: Cognitive development in children: Piaget development and learning", Journal of Res. in Science Teaching, 2(3), pp. 176-186 (1964).
8. Piaget, J., Main Trends in Psychology, London: George Allen and Unwin (1973).
9. Singer, D.G., Revenson, T.A.A., and Piaget, P., How a Child Thinks. International Universities Press, Inc., 59 Boston Post Road, Madison, CT 06443-1524 (1997).
10. Kose, U. and Arslan, A. "Forecasting chaotic time series via anfis supported by vortex optimization algorithm: Applications on electroencephalogram time series", Arabian Journal for Science and Eng., 42(8), pp. 3103-3114 (2017).
11. Gan, M., Peng, H., Peng, X., Chen, X., and Inoussa, G. "A locally linear RBF network-based state-dependent AR model for nonlinear time series modeling", Information Sciences, 180, pp. 4370-4383 (2010).
12. Wong, W.K., Xia, M., and Chu, W.C. "Adaptive neural network model for time-series forecasting", European Journal of Operational Research, 207, pp. 807-816 (2010).
13. Gentili, P.L., Gotoda, H., Dolnik, M., and Epstein, I.R. "Analysis and prediction of aperiodic hydrodynamic oscillatory time series by feed-forward neural networks, fuzzy logic, and a local nonlinear predictor", Chaos: An Interdiscip. Journal of Nonlinear Science, 25(1), 013104 (2015).
14. Chen, D. and Han, W. "Prediction of multivariate chaotic time series via radial basis function neural network", Complexity, 18(4), pp. 55-66 (2013).
15. Wu, X., Li, C., Wang, Y., Zhu, Z., and Liu, W. "Nonlinear time series prediction using iterated extended Kalman filter trained single multiplicative neuron model", Journal of Information and Comp. Science, 10, pp. 385-393 (2013).
16. Yadav, R.N., Kalra, P.K., and John, J. "Time series prediction with single multiplicative neuron model", Applied Soft Computing, 7, pp. 1157-1163 (2007). 
17. Zhao, L. and Yang, Y. "PSO-based single multiplicative neuron model for time series prediction", Expert Systems with App., 36, pp. 2805-2812 (2009).
18. Yao, J. and Liu, W. "Nonlinear time series prediction of atmospheric visibility in Shanghai", In Time Series Analysis, Modeling and Applications, Intelligent Systems Reference Library, 47, W. Pedrycz and S.-M. Chen, Eds., Springer-Verlag (2013).
19. Unler, A. "Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025", Energy Policy, 36, pp. 1937- 1944 (2008).
20. Zhao, L. and Yang, Y. "PSO-based single multiplicative  neuron model for time series prediction", Expert Systems with Apps, 36, pp. 2805-2812 (2009).
21. Weng, S.S. and Liu, Y.H. "Mining time series data for segmentation by using ant colony optimization", European Journal of Operational Research, 173, pp. 921-937 (2006).
22. Toskari, M.D. "Estimating the net electricity energy generation and demand using the ant colony optimization approach", Energy Policy, 37, pp. 1181- 1187 (2009).
23. Hong, W.C. "Application of chaotic ant swarm optimization in electric load forecasting", Energy Policy, 38, pp. 5830-5839 (2010).
24. Niu, D., Wang, Y., and Wu, D.D. "Power load forecasting using support vector machine and ant colony optimization", Expert Systems with App., 37, pp. 2531-2539 (2010).
25. Yeh, W.-C. "New parameter-free simplified swarm optimization for artificial neural network training and its application in the prediction of time series", IEEE Trans. on Neural Networks and Learning Systems, 24, pp. 661-665 (2013).
26. Nourani, V. and Andalib, G. "Wavelet based artificial intelligence approaches for prediction of hydrological time series", Australasian Conference on Artificial Life and Comp. Intelligence, pp. 422-435, Newcastle, NSW, Australia (2015).
27. Bontempi, G., Taieb, S.B., and Le Borgne, Y.-A. "Machine learning strategies for time series forecasting", In Business Intelligence (Lecture Notes in Business Information Processing), -138, M.-A. Aufaure and E. Zimanyi, Eds., Springer-Verlag (2013).
28. Hu, Y.X. and Zhang, H.T. "Prediction of the chaotic time series based on chaotic simulated annealing and support vector machine", Int. Conference on Solid State Devices and Materials Science, pp. 506-512. Macao, China (2012).
29. Liu, P. and Yao, J.A. "Application of least square support vector machine based on particle swarm optimization to chaotic time series prediction", IEEE Int. Conference on Intelligent Computing and Intelligent Systems, pp. 458-462. Shanghai, China (2009).
30. Quian, J.S., Cheng, J., and Guo, Y.N. "A novel multiple support vector machines architecture for chaotic time series prediction", Advances in Natural Computation, Lecture Notes in C.S., 4221, pp. 147- 156 (2006).
31. Yang, Z.H.O., Wang, Y.S., Li, D.D., and Wang, C.J. "Predict the time series of the parameter-varying chaotic system based on reduced recursive lease square support vector machine", IEEE Int. Conference on Artificial Intelligence and Comp. Intelligence, pp. 29-34, Shanghai, China (2009).
32. Zhang, J.S., Dang, J.L., and Li, H.C. "Local support vector machine prediction of spatiotemporal chaotic time series", Acta Physica Sinica, 56, pp. 67-77 (2007).
33. Farooq, T., Guergachi, A., and Krishnan, S. "Chaotic time series prediction using knowledge based Green's kernel and least-squares support vector machines", IEEE Int. Conference on Systems, Man and Cybernetics,pp. 2669-2674, Montreal, Cook Islands (2007).
34. Shi, Z.W. and Han, M. "Support vector echo-state machine for chaotic time-series prediction", IEEE Trans. on Neural Networks, 18, pp. 359-372 (2007).
35. Li, H.T. and Zhang, X.F. "Precipitation time series predicting of the chaotic characters using support vector machines", Int. Conference on Information Management, Innovation Management and Indus. Eng., pp. 407-410, Xian, China (2009).
36. Zhu, C.H., Li, L.L., Li, J.H., and Gao, J.S. "Shortterm wind speed forecasting by using chaotic theory and SVM", Applied Mechanics and Materials, 300- 301, pp. 842-847 (2013).
37. Ren, C.-X., Wang, C.-B., Yin, C.-C., Chen, M., and Shan, X. "The prediction of short-term traffic flow based on the niche genetic algorithm and BP neural network", 2012 Int. Conference on Information Technology and Software Engineering, pp. 775-781, Beijing, China (2013).
38. Ding, C., Wang, W., Wang, X., and Baumann, M. "A neural network model for driver's lane-changing trajectory prediction in urban traffic flow", Mathematical Problems in Engineering (Online) (2013). DOI: 10.1155/2013/967358.
39. Yin, H., Wong, S.C., Xu, J., and Wong, C.K. "Urban traffic  flow prediction using a fuzzy-neural approach", Transportation Research Part-C: Emerging Technologies, 10, pp. 85-98 (2002).
40. Dunne, S. and Ghosh, B. "Weather adaptive traffic prediction using neurowavelet models", IEEE Trans. on Intelligent Transportation Systems, 14, pp. 370- 379 (2013).
41. Pulido, M., Melin, P., and Castillo, O. "Particle swarm optimization of ensemble neural networks with fuzzy aggregation for time series prediction of the Mexican stock exchange", Information Sciences, 280, pp. 188-204 (2014).
42. Huang, D.Z., Gong, R.X., and Gong, S. "Prediction of wind power by chaos and BP artificial neural networks approach based on genetic algorithm", Journal of Electrical Eng. and Tech., 10(1), pp. 41-46, (2015).
43. Jiang, P., Qin, S., Wu, J., and Sun, B. "Time series analysis and forecasting for wind speeds using support vector regression coupled with artificial intelligent algorithms", Mathematical Prob. in Eng., Article ID 939305 (2015).
44. Doucoure, B., Agbossou, K., and Cardenas, A. "Time series prediction using artificial wavelet neural network and multi-resolution analysis: Application to wind speed data", Renewable Energy, 92, pp. 202- 211 (2016).
45. Chandra, R. "Competition and collaboration in cooperative coevolution of Elman recurrent neural networks for time-series prediction", IEEE Trans. on Neural Networks and Learning Systems, 26(12), pp. 3123-3136 (2015).
46. Chai, S.H. and Lim, J.S. "Forecasting business cycle with chaotic time series based on neural network with weighted fuzzy membership functions", Chaos, Solitons and Fractals, 90, pp. 118-126 (2016).
47. Seo, Y., Kim, S., Kisi, O., and Singh, V.P. "Daily water level forecasting using wavelet decomposition and artificial intelligence techniques", Journal of Hydrology, 520, pp. 224-243 (2015).
48. Marzban, F., Ayanzadeh, R., and Marzban, P. "Discrete time dynamic neural networks for predicting chaotic time series", Journal of Artificial Intelligence, 7(1), p. 24 (2014).
49. Okkan, U. "Wavelet neural network model for reservoir in flow prediction", Scientia Iranica, 19(6), pp. 1445-1455 (2012).
50. Zhou, T., Gao, S., Wang, J., Chu, C., Todo, Y., and Tang, Z. "Financial time series prediction using a dendritic neuron model", Knowledge-Based Systems, 105, pp. 214-224 (2016).
51. Wang, L., Zou, F., Hei, X., Yang, D., Chen, D., Jiang, Q., and Cao, Z. "A hybridization of teachinglearning- based optimization and differential evolution for chaotic time series prediction", Neural Comp. and App., 25(6), pp. 1407-1422 (2014).
52. Heydari, G., Vali, M., and Gharaveisi, A.A. "Chaotic time series prediction via artificial neural square fuzzy inference system", Expert Systems with App., 55, pp. 461-468 (2016).
53. Wang, L., Zeng, Y., and Chen, T. "Back propagation neural network with adaptive differential evolution algorithm for time series forecasting", Expert Systems with App., 42(2), pp. 855-863 (2015).
54. Catalao, J.P.S., Pousinho, H.M.I., and Mendes, V.M.F. "Hybrid wavelet-PSO-ANFIS approach for short-term electricity prices forecasting", IEEE Trans. on Power Systems, 26(1), pp. 137-144 (2011).
55. Patra, A., Das, S., Mishra, S.N., and Senapati, M.R. "An adaptive local linear optimized radial basis functional neural network model for financial time series prediction", Neural Comp. and App., 28(1), pp. 101-110 (2017).
56. Ravi, V., Pradeepkumar, D., and Deb, K. "Financial time series prediction using hybrids of chaos theory, multi-layer perceptron and multi-objective evolutionary algorithms", Swarm and Evolutionary Computation, 36, pp. 136-149 (2017).
57. Mendez, E., Lugo, O., and Melin, P. "A competitive modular neural network for long-term time series forecasting", In Nature-Inspired Design of Hybrid Intelligent Systems, pp. 243-254, Springer Int. Publishing (2017).
58. Carpenter, G.A. "Neural network models for pattern recognition and associative memory", Neural Networks, 2(4), pp. 243-257 (1989).
59. Cochocki, A. and Unbehauen, R., Neural Networks for Optimization and Signal Processing, John Wiley & Sons, Inc. (1993).
60. Miller, W.T., Sutton, R.S., and Werbos, P.J., Neural Networks for Control, MIT Press (1995).
61. Ripley, B.D. "Neural networks and related methods for classification", Journal of the Royal Statistical Society, Series-B (Methodological), pp. 409-456 (1994).
62. Basheer, I.A. and Hajmeer, M. "Artificial neural networks: fundamentals, computing, design and application", Journal of Microbiological Methods, 43, pp. 3-31 (2000).
63. Badri, A., Ameli, Z., and Birjandi, A.M. "Application of artificial neural networks and fuzzy logic methods for short term load forecasting", Energy Procedia, 14, pp. 1883-1888 (2012).
64. Ghorbanian, J., Ahmadi, M., and Soltani, R. "Design predictive tool and optimization of journal bearing using neural network model and multi-objective genetic algorithm", Scientia Iranica, 18(5), pp. 1095- 1105 (2011).
65. Gholizadeh, S. and Seyedpoor, S.M. "Shape optimization of arch dams by metaheuristics and neural networks for frequency constraints", Scientia Iranica, 18(5), pp. 1020-1027 (2011).
66. Firouzi, A. and Rahai, A. "An integrated ANNGA for reliability based inspection of concrete bridge decks considering extent of corrosion-induced cracks and life cycle costs", Scientia Iranica, 19(4), pp. 974- 981 (2012).
67. Shahreza, M.L., Moazzami, D., Moshiri, B., and Delavar, M.R. "Anomaly detection using a selforganizing map and particle swarm optimization", Scientia Iranica, 18(6), pp. 1460-1468 (2011).
68. Ruck, D.W., Rogers, S.K., and Kabrisky, M. "Feature selection using a multilayer perceptron", Journal of Neural Network Comp., 2(2), pp. 40-48 (1990).
69. Kose, U. and Arslan, A. "Optimization of selflearning in computer engineering courses: An intelligent software system supported by artificial neural network and vortex optimization algorithm", Computer Applications in Engineering Education, 25(1), pp. 142-156 (2017).
70. McCulloch, W.S. and Pitts, W. "A logical calculus of the ideas immanent in nervous activity", Bulletin of Mathematical Biology, January, 52, pp. 99-115 (1943). Reprint: Bulletin of Mathematical Biophysics, 5, pp. 115-133 (1990).
71. Anderson, D. and McNeill, G. "Artificial neural networks technology", A DACS state-of-the-art report. Kaman Sciences Corporation, 258, PP. 13502-13462 (1992).
72. Ugur, A. and Kinaci, A.C. "A web-based tool for teaching neural network concepts", Computer App. in Engineering Educ., 18(3), pp. 449-457 (2010).
73. Yegnanarayana, B., Artificial Neural Networks, PHI Learning Pvt. Ltd (2009).
74. Demir, A. and Kose, U. "Solving optimization problems via vortex optimization algorithm and cognitive development optimization algorithm", Broad Research in Artificial Intelligence and Neuroscience, 7(4), pp. 23-42 (2016).
75. Kose, U. and Arslan, A. "Intelligent e-learning system for improving students' academic achievements in computer programming courses", Int. Journal of Engineering Educ., 32(1), pp. 185-198 (2016).
76. Blum, C. and Li, X. "Swarm intelligence in optimization", In Swarm Intelligence, C. Blum and D. Merkle, Eds., Springer Berlin Heidelberg (2008).
77. Engelbrecht, A.P., Fundamentals of Computational Swarm Intelligence, John Wiley & Sons (2006).
78. Bonabeau, E., Dorigo, M., and Theraulaz, G., Swarm Intelligence: From Natural to Artificial Systems (No. 1), Oxford Univ. Press (1999).
79. Panigrahi, B.K., Shi, Y., and Lim, M.H. (Eds.)., Handbook of Swarm Intelligence: Concepts, Principles and Applications, 8, Springer Sci. & Business Media (2011).
80. Fukuyama, Y. "Fundamentals of particle swarm optimization techniques", In Modern Heuristic Optimization Techniques: Theory and Applications to Power Systems, K.Y. Lee and M.A. El-Sharkawi, Eds., John Wiley & Sons, Hoboken, N.J., USA (2008).
81. Bonabeau, E., Dorigo, M., and Theraulaz, G. "Inspiration for optimization from social insect behaviour", Nature, 406(6791), pp. 39-42 (2000).
82. Kennedy, J. "Particle swarm optimization", In Encyclopedia of Machine Learning, C. Sammut and G.I. Webb, Eds., Springer-US (2011).
83. Dorigo, M. and Blum, C. "Ant colony optimization theory: A survey", Theoretical Computer Science, 344(2), pp. 243-278 (2005).
84. Karaboga, D., Artificial Intelligence Optimization Algorithms, Nobel Publishing, Turkey, ISBN 975- 6574 (2004).
85. DataMarket "DataMarket- find, understand and share data", https://datamarket.com/ Online. Retrieved on 12th July (2016).
86. Scholarpedia "Mackey-glass equation", http:// www.scholarpedia.org/article/Mackey-Glassequation Online. Retrieved on 12th July (2016).
87. OTexts.org. "Evaluating forecast accuracy", https://www.otexts.org/fpp/2/5 Online. Retrieved on 16th July (2016).
88. Eberhart, R.C. Kennedy, J. "A new optimizer using particle swarm theory", Sixth International Symposium on Micro Machine and Human Science, pp. 39- 43 (1995).
89. Kennedy, J. "The particle swarm: social adaptation of knowledge", IEEE Int. Conference on Evol. Comp., pp. 303-308, IEEE (1997).
90. Yang, X.S. and Deb, S. "Cuckoo search via Levy  flights", World Congress on Nature & Biologically Ins. Comp., pp. 210-214, IEEE (2009).
91. Yang, X.S. and Deb, S. "Cuckoo search: recent advances and applications", Neural Comp. and App., 24(1), pp. 169-174 (2014).
92. Yang, X.S., Nature-Inspired Metaheuristic Algorithms, Luniver Press (2010).
93. Yang, X.S. "Fire y algorithms for multimodal optimization", In Stochastic Algorithms: Foundations and Applications, O. Watanabe and T. Zeugmann, Eds., Springer Berlin Heidelberg (2009).
94. Yang, X.S. "A new metaheuristic bat-inspired algorithm", In Nature Inspired Cooperative Strategies for Optimization, (NICSO 2010), J.R. Gonzelez, D.A., Pelta, C., Cruz, G. Terrazas, and N. Krasnogor, Eds., Springer Berlin Heidelberg (2010).
95. Yang, X.S. and Hossein Gandomi, A. "Bat algorithm: a novel approach for global engineering optimization", Engineering Computations, 29(5), pp. 464-483 (2012).
96. Dasgupta, S. and Osogami, T. "Nonlinear Dynamic Boltzmann Machines for Time-Series Prediction", In AAAI, 31(12), pp. 1833-1839 (2017).
97. Kim, K.J. "Financial time series forecasting using support vector machines", Neurocomputing, 55(1), pp. 307-319 (2003).
98. Hassan, M.R. and Nath, B. "Stock market forecasting using hidden Markov model: a new approach. In intelligent systems design and applications", ISDA'05. Proceedings. 5th Int. Conference on IEEE, pp. 192- 196 (2005).
99. Brahim-Belhouari, S. and Bermak, A. "Gaussian process for nonstationary time series prediction", Comp. Statistics & Data Analysis, 47(4), pp. 705-712 (2004).
100. Giarratano, J.C. and Riley, G., Expert Systems, PWS Publishing Co. (1998).
101. Turban, E. and Frenzel, L.E., Expert Systems and Applied Artificial Intelligence, Prentice Hall Professional Tech. Reference (1992).
102. David, J.M., Krivine, J.P., and Simmons, R. Eds., Second Generation Expert Systems, Springer Sci.& Business Media (2012).